Amit Verma ¹, Srishti Gupta ¹, Iqbaldeep Kaur ¹

Affiliations
1 Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali – 140307, Punjab, IN

Abstract

Objectives: Inconsistency detection is one of the major challenges in source code for the software developers. There is the need of consistent identifiers to reduce the code inconsistencies. So, developers should either have the knowledge to create conceptual identifiers or the knowledge to detect the inconsistencies in source code. Methods/Statistical Analysis: There is the availability of a list of tools for the detection of different types of inconsistencies. But the existing tools are not much appropriate for Semantic, Syntactic Inconsistencies and Part of Speech tagging. Findings: In the paper, an autonomous tool Automatic Bad Code Detector (ABCD) is developed to detect semantic, syntactic and part of speech inconsistency in the source code. ABCD tool identifies the inconsistencies in the source code based on the detected Code Clones. These Clones are detected by matching the test code with Code Repository. A java project based code repository is considered for experimentation. ABCD is evaluated for different java projects in order to find inconsistencies in source code. In ABCD tool main inconsistency detector are Ant Colony Optimization and Neural Network Back Propagation algorithm. Further, ABCD is useful in re-implementing the new versions of the java code. Applications/Improvements: The current concept is evaluated for the Semantic, Syntactic, POS-Word and POS-Phrase inconsistencies based on evaluation parameter of precision. The efficiency of ABCD is evaluated as an overall value for the precision, recall and f-measure.

Keywords

Ant Colony Optimization, Automatic Bad Code Detector, Code Repository, Inconsistency Detection, Neural Network Back Propagation Algorithm.

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Amit Verma ¹, Asha Gill ¹, Iqbaldeep Kaur ²

Affiliations
1 Department of Computer Science and Engineering, CGC Landran, Mohali - 140307, Punjab, IN
2 Department of Computer Science and Engineering, Chandigarh University, Mohali - 140413, Punjab, IN

Abstract

Objectives: Effective data processing for fast retrieval of information has become a burning issue. A modern document contains not only text but images, video, audio as well. In this paper, a brief history of storage devices from the Vedic period to the world of digitization with some important inventions has been presented. Method/Statistical Analysis: It also includes the discussion on how data is transformed for the decision making process along with preprocessing techniques. Findings: A comparative analysis has been done of various techniques, their specific algorithms, uses, pros, limitations and applications where these can be implemented. It helps to give us an insight about these techniques. Finally experimental results on three different algorithms (Id3, CHAID, Naive Bayes) using Rapid Miner have been evaluated to compare their performance based on three parameters (accuracy, precision and recall). Applications/Improvements: The empirical results show the ID3 as more accurate than others with 95.95% accuracy while CHAID shows 89.11% and Naive Bayes classified 81.77% data accurately. *

Keywords

CHAID, Development of Storage Devices, ID3, Information Retrieval, Rapid Miner, Retrieval Techniques, Visualization.

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Amit Verma ¹, Iqbaldeep Kaur ¹, Namita Arora ²

Affiliations
1 Department of Computer Science and Engineering, CGC Landran, Mohali - 140307, Punjab, IN
2 Department of Computer Science and Engineering, Chandigarh University, Mohali - 140413, Punjab, IN

Abstract

Background/Objectives: This paper emphasizes the evolution of data processing adroitness to advanced data processing taxonomy from Mesolithic to recent years and a comparative study of prevailing tools/techniques which are useful for mainly the analysis of the bulky data. Methods/Statistical Analysis: There are various kinds of methods adapted by researchers for analysis of large amount of data. Each method varies on the basis of their different parameters and datasets according to their needs. These methods are implemented on HDFS, Mapreduce and Hadoop environment with integration of R tool. Some Methods are enhanced by the sentimental analysis through NLP which increase the performance of density analysis. Findings: The data or associated facts have been in existence right with the birth of human species. It commenced with manual illustration and gradually advanced through current state-of the art storage and processing. Big data involves novel techniques to manage information within limited run time. Big data is acutely beneficial in ventures growth, society incumbency and scientific research. The paper provides an overview of state of the art and focuses on the usage of conventional tools as well as advanced tools and techniques for effective information extraction. Applications/Improvements: To handle this prodigious data, there is a need to upgrade from the traditional data filtering techniques and adopt the new big data diagnostic tools.

Keywords

Big Data, Data Analysis, Data Mining, Evolution, Techniques, Tools

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Amit Verma ¹, Iqbaldeep Kaur ², Inderjeet Singh ¹

Affiliations
1 Department of Computer Science and Engineering, Chandigarh University, Mohali - 140413, Punjab, IN
2 Department of Computer Science and Engineering, Chandigarh Engineering College, Mohali - 140307, Punjab, IN

Abstract

Background/Objectives: There are a lot of information retrieval techniques available for getting information from different kinds of sources. Main aim of this paper is to improve information retrieval activities to a higher level. Different methods for information retrieval have been studied and discussed. It involves use of Fuzzy Ontology Generation framework (FOGA) framework along with Formal Concept Analysis (FCA) based clustering and keyword matching approach. Hidden Markov Model has been used for retrieval of data from search engines in an intelligent and efficient way for correct identification and retrieval from the database. Classification algorithm has been used for detection of community and conversion of large community graph to sub community graph for its better study and usage. Findings: It has been found that fuzzy ontology generation framework can automatically generate the fuzzy ontology which is very hard and time consuming task otherwise. Clustering of data can be done using the technique of formal concept analysis along with keyword matching method. There is a large amount of data available under similar words but with different meanings. So there has been a lot of problem in retrieval of exact data as required in a very short amount of time. In this case Hidden Markov Model can be used which can find the non-observable or hidden stochastic process from the observable stochastic process. Application/Improvements: Generalized Expectation-Maximization algorithm used with Hidden Markov Model can find unknown parameters. By adding frequency tracking algorithm along with Hidden Markov Model, we can also track audible data from a large database. Community detection algorithm along with Informap and Bigclam algorithms used with Hidden Markov Model will increase the modularity of data. Its applications include use of information retrieval of different types of data in an extremely faster and efficient way.

Keywords

Classification, Clustering, Community Detection Algorithm, Fuzzy Ontology Generation Framework, Formal Concept Analysis, Hidden Markov Model

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Amit Verma ¹, Iqbaldeep Kaur ¹, Amandeep Kaur ¹

Affiliations
1 Department of Computer Science and Engineering, Chandigarh Engineering College, Mohali - 140307, Punjab, IN

Abstract

Objective/Background: This paper highlights the extension of access data to data mining from passing year to recent. Main aim of this paper is comparative study of tools/techniques/algorithms which are used for analysis of huge amount of data. Methods/Statistical Analysis: Different methods of data mining has been studied and discussed which include decision tree, neural network, regression, clustering techniques are implemented on different tools for fraud detection. Different algorithms Adaboost, page rank, K-means used for data mining are also discussed. For generate relevant information from data streams, frequent pattern generation tree algorithm is also implemented and discussed. Findings: Out of so many available algorithms decision tree has been found out to be the most suitable for mining data provided the data is restricted to some thousand of entries. The most prominent feature as its advantage lies in its clear illustration in the form of graphical tree with inherent tree structure capability. However the concern about ambiguity should be carefully dealt with maintains consistency. Applications: For the extraction of the relevant data, data mining is helpful in various ways. The various areas where data mining is being used have also been discussed in the paper. Future Scope: The scope of the paper extends from an exhaustive survey and analysis of all available empirical and conceptual techniques and tools in the area of data mining.

Keywords

Association Rule Mining, Classification, Clustering, Data, Data Mining, Decision tree, Neural Network.

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Iqbaldeep Kaur ¹, Dimple Nagpal ¹, Amit Verma ¹

Affiliations
1 Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali - 140307, Punjab, IN

Abstract

Objectives: Component based development commences the field of reusing the component rather than redeveloping it.To reuse the component, firstly the retrieval of component through repository has been done. Methods/Statistical analysis: There are many ways to retrieve the component but every scheme has its own pros and cons. To retrieve the best component, the relationships between components must be there. This paper presents the overview of whole process of component retrieval using ontology. Findings: Ontology helps in effective retrieval by providing relationships between concepts (classes), interfaces etc. Application/Improvements: Additionally this paper reviews what the component is, innovation in ontology, what are the reusable assets in component and how the component are formally described using languages are presented.The goal of ontology based retrieval of component is that best component retrieves from the interconnected repositories..

Keywords

Algorithm, Ontology Languages, Retrieval.

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Iqbaldeep Kaur ¹, Gifty Aggarwal ¹, Amit Verma ¹

Affiliations
1 Computer Science and Engineering, Chandigarh Engineering College, Landran - 140307, Punjab, IN

Abstract

Objective: There is the existence of a variety of plants on this earth surface that plays enormous role in human life. But various factors are there that can destroy plant growth like weather conditions, non-availability of accurate resources, plant diseases and lack of expert knowledge to care plants. Statistical Analysis: Plant diseases are one of the major factors responsible for the reduction of plant growth. In the ancient years, it was not easy to detect the plant diseases on time. But in this computing era, digital image processing rapidly developed that it can be used for various real life applications. Findings: In this research work, plant leaf diseases are detected and classified using the image processing techniques. The fundamental steps of image processing and leaf disease detection and final optimization are used in this work. Here, image acquisition is performed by considering RGB colour based disease affected leaf image. Image contrast is enhanced using Histogram Equalization. Image segmentation is performed with K means clustering. Image feature extraction is performed to extract the features of leaf disease symptoms by maintaining Grey Level Occurrence Matrices. Support Vector Machine is used for the leaf disease detection & classification and finally ant colony optimization is applied for the optimization of concept. Applications/Improvements: For the experimentation, dataset of plant leaf affected from bacterial disease ‘Bacterial Blight’ and fungal diseases ‘Alternaria alternata’, ‘Fungal Leaf Spot’ and ‘Fungus Anthracnose’ are considered. The proposed concept is also evaluated by comparative analysis with the existing concepts of SVM and Improved SVM.

Keywords

Ant Colony Optimization, Histogram Equalization, Image Processing, K-Means Clustering,Plant Leaf Disease Detection, , Support Vector Machine.

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R. K. Bawa ¹, Iqbaldeep Kaur ²

Affiliations
1 Department of Computer Science, Punjabi University, Patiala - 147002, Punjab, IN
2 Department of Computer Science and Engineering, Punjabi University, Patiala – 147002, Punjab, IN

Abstract

Objectives: Software development is largely being transformed from traditional model based approach to plug and play component based approach which is required for enhancing reusability of the system for robust development. Methods/ Statistical Analysis: Tremendous techniques and models have been used and adopted for component qualification and adaptation using selection, construction, analysis and evaluation algorithms. The major task has always been to find an optimum centric approach to trade off the advantages of customized development and development using reusable components. Findings: CBSE environment and development by using various components as object, detail description and tools with platforms have been presented. The classification algorithms showed robustness of component, based on various extracted module. The ontology based algorithm is one of the robust techniques in the retrieval process. Futuristic implementation will emerge with this segregation cum classification scenario in a single unit that is to refine component presentation, management and retrieval. Applications/Improvements: Initially IBM Company adopted the concept of CBSE by using the System Object Model (SOM). The functions, libraries and UNIX pipes are some other examples of such approaches. The scope of the improvement, as far as findings and further work is concerned, lies with the selection, classification and retrieval scheme for component repositories. This has been improved with different representations of meta data of the component such as domain, interface, services and other semantic information.

Keywords

COM, Component based, CORBA, Component Life Cycle, EJP, Software Component Models, Software Engineering.

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